Unsupervised estimation of Dirichlet smoothing parameters

A standard approach for determining a Dirichlet smoothing parameter is to choose a value which maximizes a retrieval performance metric using training data consisting of queries and relevance judgments. There are, however, situations where training data does not exist or the queries and relevance judgments do not reflect typical user information needs for the application. We propose an unsupervised approach for estimating a Dirichlet smoothing parameter based on collection statistics. We show empirically that this approach can suggest a plausible Dirichlet smoothing parameter value in cases where relevance judgments cannot be used.